Goal is to identify transcription factor acitivies that are different across conditions that may correlate with our treatment conditions.
One thing that occurs to me in this analysis is that I may want to use the higher resolution annotations from the “CD4” annotation rather than relying on the all cells annotation. Will repeat at a later date along with prop analysis.
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(Signac))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(patchwork))
results<-readRDS("~/gibbs/DOGMAMORPH/Ranalysis/Objects/20230606FinalObj.rds")
DefaultAssay(results)<-"chromvar"FixMotifID<-function(markeroutput, seuratobj){
markeroutput$genes<-ConvertMotifID(seuratobj, assay="ATAC", id=rownames(markeroutput))
return(markeroutput)
}##Cytotoxic T cells
Interesting differences here in JunFos and Eomes that warrant further looks.
Cyotoxic_T_Meth_v_Nal<-FindMarkers(results, "Methadone","Naltrexone",subset.ident = "Cytotoxic_T", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.2, drop = FALSE]): NaNs producedCyotoxic_T_Bup.Nalo_v_Nal<-FindMarkers(results, "Bup.Nalo","Naltrexone",subset.ident = "Cytotoxic_T", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.2, drop = FALSE]): NaNs producedCyotoxic_T_Meth_v_Bup.Nalo<-FindMarkers(results, "Methadone","Bup.Nalo",subset.ident = "Cytotoxic_T", group.by = "Treatment")
Cyotoxic_T_Meth_v_Nal<-FixMotifID(Cyotoxic_T_Meth_v_Nal, results)
Cyotoxic_T_Bup.Nalo_v_Nal<-FixMotifID(Cyotoxic_T_Bup.Nalo_v_Nal, results)
Cyotoxic_T_Meth_v_Bup.Nalo<-FixMotifID(Cyotoxic_T_Meth_v_Bup.Nalo, results)
#differences seen here seem to be mostly housekeeping, this is the hard one to look at anyways
DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Meth_v_Nal)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Bup.Nalo_v_Nal)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Meth_v_Bup.Nalo)#the real show is looking at it by timepoint since this is what we actally identified it from
Cyotoxic_T_Meth_3_v_0<-FindMarkers(subset(results, Treatment=="Methadone"), 3,0,subset.ident = "Cytotoxic_T", group.by = "Timepoint")
Cyotoxic_T_Bup.Nalo_3_v_0<-FindMarkers(subset(results, Treatment=="Bup.Nalo"), 3,0,subset.ident = "Cytotoxic_T", group.by = "Timepoint")
Cyotoxic_T_Nal_3_v_0<-FindMarkers(subset(results, Treatment=="Naltrexone"), 3,0,subset.ident = "Cytotoxic_T", group.by = "Timepoint")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs produced
## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedCyotoxic_T_Meth_3_v_0<-FixMotifID(Cyotoxic_T_Meth_3_v_0, results)
Cyotoxic_T_Bup.Nalo_3_v_0<-FixMotifID(Cyotoxic_T_Bup.Nalo_3_v_0, results)
Cyotoxic_T_Nal_3_v_0<-FixMotifID(Cyotoxic_T_Nal_3_v_0, results)
#Bup.Nalo has some interesting changes here, looks liekk they're mabye losing some effector profile. JunFos also different in Nalo, methadone shows nothing which is interesting
DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Meth_3_v_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Bup.Nalo_3_v_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Nal_3_v_0)#trying just comparing the time 0 and time 3 comparison
#interesting Jun Fos changes at time 3, though some of those also occur at time zero. Might be a shift of dimers which could be interesting.
Cyotoxic_T_Meth_v_Nal_3<-FindMarkers(subset(results, Timepoint==3), "Methadone","Naltrexone",subset.ident = "Cytotoxic_T", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.2, drop = FALSE]): NaNs producedCyotoxic_T_Bup.Nalo_v_Nal_3<-FindMarkers(subset(results, Timepoint==3), "Bup.Nalo","Naltrexone",subset.ident = "Cytotoxic_T", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.2, drop = FALSE]): NaNs producedCyotoxic_T_Meth_v_Bup.Nalo_3<-FindMarkers(subset(results, Timepoint==3), "Methadone","Bup.Nalo",subset.ident = "Cytotoxic_T", group.by = "Treatment")
Cyotoxic_T_Meth_v_Nal_0<-FindMarkers(subset(results, Timepoint==0), "Methadone","Naltrexone",subset.ident = "Cytotoxic_T", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.2, drop = FALSE]): NaNs producedCyotoxic_T_Bup.Nalo_v_Nal_0<-FindMarkers(subset(results, Timepoint==0), "Bup.Nalo","Naltrexone",subset.ident = "Cytotoxic_T", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.2, drop = FALSE]): NaNs producedCyotoxic_T_Meth_v_Bup.Nalo_0<-FindMarkers(subset(results, Timepoint==0), "Methadone","Bup.Nalo",subset.ident = "Cytotoxic_T", group.by = "Treatment")
Cyotoxic_T_Meth_v_Nal_3<-FixMotifID(Cyotoxic_T_Meth_v_Nal_3, results)
Cyotoxic_T_Bup.Nalo_v_Nal_3<-FixMotifID(Cyotoxic_T_Bup.Nalo_v_Nal_3, results)
Cyotoxic_T_Meth_v_Bup.Nalo_3<-FixMotifID(Cyotoxic_T_Meth_v_Bup.Nalo_3, results)
Cyotoxic_T_Meth_v_Nal_0<-FixMotifID(Cyotoxic_T_Meth_v_Nal_0, results)
Cyotoxic_T_Bup.Nalo_v_Nal_0<-FixMotifID(Cyotoxic_T_Bup.Nalo_v_Nal_0, results)
Cyotoxic_T_Meth_v_Bup.Nalo_0<-FixMotifID(Cyotoxic_T_Meth_v_Bup.Nalo_0, results)
DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Meth_v_Nal_3)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Bup.Nalo_v_Nal_3)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Meth_v_Bup.Nalo_3)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Meth_v_Nal_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Bup.Nalo_v_Nal_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Cyotoxic_T_Meth_v_Bup.Nalo_0)limited differences in this population mainly in SPI factors. Not worth following up on as such.
#fewer gene differences, but they do appear to be immune related. Could be useful to see via GSEA
Naive_T_Meth_v_Nal<-FindMarkers(results, "Methadone","Naltrexone",subset.ident = "Naive_CD4_T_3", group.by = "Treatment")
Naive_T_Bup.Nalo_v_Nal<-FindMarkers(results, "Bup.Nalo","Naltrexone",subset.ident = "Naive_CD4_T_3", group.by = "Treatment")
Naive_T_Meth_v_Bup.Nalo<-FindMarkers(results, "Methadone","Bup.Nalo",subset.ident = "Naive_CD4_T_3", group.by = "Treatment")
Naive_T_Meth_v_Nal<-FixMotifID(Naive_T_Meth_v_Nal, results)
Naive_T_Bup.Nalo_v_Nal<-FixMotifID(Naive_T_Bup.Nalo_v_Nal, results)
Naive_T_Meth_v_Bup.Nalo<-FixMotifID(Naive_T_Meth_v_Bup.Nalo, results)
DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Meth_v_Nal)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Bup.Nalo_v_Nal)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Meth_v_Bup.Nalo)#Now we're cooking with gas, these appear to have differences in activation! Some key transcription factors are also changed like ETS and ZEB that might be intersting at an ATAC level.
Naive_T_Meth_3_v_0<-FindMarkers(subset(results, Treatment=="Methadone"), 3,0,subset.ident = "Naive_CD4_T_3", group.by = "Timepoint")
Naive_T_Bup.Nalo_3_v_0<-FindMarkers(subset(results, Treatment=="Bup.Nalo"), 3,0,subset.ident = "Naive_CD4_T_3", group.by = "Timepoint")
Naive_T_Nal_3_v_0<-FindMarkers(subset(results, Treatment=="Naltrexone"), 3,0,subset.ident = "Naive_CD4_T_3", group.by = "Timepoint")
Naive_T_Meth_3_v_0<-FixMotifID(Naive_T_Meth_3_v_0, results)
Naive_T_Bup.Nalo_3_v_0<-FixMotifID(Naive_T_Bup.Nalo_3_v_0, results)
Naive_T_Nal_3_v_0<-FixMotifID(Naive_T_Nal_3_v_0, results)
DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Meth_3_v_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Bup.Nalo_3_v_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Nal_3_v_0)#trying just comparing the time 0 and time 3 comparison
Naive_T_Meth_v_Nal_3<-FindMarkers(subset(results, Timepoint==3), "Methadone","Naltrexone",subset.ident = "Naive_CD4_T_3", group.by = "Treatment")
Naive_T_Bup.Nalo_v_Nal_3<-FindMarkers(subset(results, Timepoint==3), "Bup.Nalo","Naltrexone",subset.ident = "Naive_CD4_T_3", group.by = "Treatment")
Naive_T_Meth_v_Bup.Nalo_3<-FindMarkers(subset(results, Timepoint==3), "Methadone","Bup.Nalo",subset.ident = "Naive_CD4_T_3", group.by = "Treatment")
Naive_T_Meth_v_Nal_0<-FindMarkers(subset(results, Timepoint==0), "Methadone","Naltrexone",subset.ident = "Naive_CD4_T_3", group.by = "Treatment")
Naive_T_Bup.Nalo_v_Nal_0<-FindMarkers(subset(results, Timepoint==0), "Bup.Nalo","Naltrexone",subset.ident = "Naive_CD4_T_3", group.by = "Treatment")
Naive_T_Meth_v_Bup.Nalo_0<-FindMarkers(subset(results, Timepoint==0), "Methadone","Bup.Nalo",subset.ident = "Naive_CD4_T_3", group.by = "Treatment")
Naive_T_Meth_v_Nal_3<-FixMotifID(Naive_T_Meth_v_Nal_3, results)
Naive_T_Bup.Nalo_v_Nal_3<-FixMotifID(Naive_T_Bup.Nalo_v_Nal_3, results)
Naive_T_Meth_v_Bup.Nalo_3<-FixMotifID(Naive_T_Meth_v_Bup.Nalo_3, results)
Naive_T_Meth_v_Nal_0<-FixMotifID(Naive_T_Meth_v_Nal_0, results)
Naive_T_Bup.Nalo_v_Nal_0<-FixMotifID(Naive_T_Bup.Nalo_v_Nal_0, results)
Naive_T_Meth_v_Bup.Nalo_0<-FixMotifID(Naive_T_Meth_v_Bup.Nalo_0, results)
DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Meth_v_Nal_3)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Bup.Nalo_v_Nal_3)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Meth_v_Bup.Nalo_3)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Meth_v_Nal_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Bup.Nalo_v_Nal_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Naive_T_Meth_v_Bup.Nalo_0)Jun::FOS comes up here as well, but there are also relatively few difference and its unlikely to be worth following up.
Mono_Meth_v_Nal<-FindMarkers(results, "Methadone","Naltrexone",subset.ident = "CD14+_Mono", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs produced## Warning in mean.fxn(object[features, cells.2, drop = FALSE]): NaNs producedMono_Bup.Nalo_v_Nal<-FindMarkers(results, "Bup.Nalo","Naltrexone",subset.ident = "CD14+_Mono", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs produced
## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedMono_Meth_v_Bup.Nalo<-FindMarkers(results, "Methadone","Bup.Nalo",subset.ident = "CD14+_Mono", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs produced
## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedMono_Meth_v_Nal<-FixMotifID(Mono_Meth_v_Nal, results)
Mono_Bup.Nalo_v_Nal<-FixMotifID(Mono_Bup.Nalo_v_Nal, results)
Mono_Meth_v_Bup.Nalo<-FixMotifID(Mono_Meth_v_Bup.Nalo, results)
DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Meth_v_Nal)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Bup.Nalo_v_Nal)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Meth_v_Bup.Nalo)Mono_Meth_3_v_0<-FindMarkers(subset(results, Treatment=="Methadone"), 3,0,subset.ident = "CD14+_Mono", group.by = "Timepoint")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedMono_Bup.Nalo_3_v_0<-FindMarkers(subset(results, Treatment=="Bup.Nalo"), 3,0,subset.ident = "CD14+_Mono", group.by = "Timepoint")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs produced
## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedMono_Nal_3_v_0<-FindMarkers(subset(results, Treatment=="Naltrexone"), 3,0,subset.ident = "CD14+_Mono", group.by = "Timepoint")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedMono_Meth_3_v_0<-FixMotifID(Mono_Meth_3_v_0, results)
Mono_Bup.Nalo_3_v_0<-FixMotifID(Mono_Bup.Nalo_3_v_0, results)
Mono_Nal_3_v_0<-FixMotifID(Mono_Nal_3_v_0, results)
DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Meth_3_v_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Bup.Nalo_3_v_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Nal_3_v_0)Mono_Meth_v_Nal_3<-FindMarkers(subset(results, Timepoint==3), "Methadone","Naltrexone",subset.ident = "CD14+_Mono", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs produced
## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedMono_Bup.Nalo_v_Nal_3<-FindMarkers(subset(results, Timepoint==3), "Bup.Nalo","Naltrexone",subset.ident = "CD14+_Mono", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs produced
## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedMono_Meth_v_Bup.Nalo_3<-FindMarkers(subset(results, Timepoint==3), "Methadone","Bup.Nalo",subset.ident = "CD14+_Mono", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs produced
## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedMono_Meth_v_Nal_0<-FindMarkers(subset(results, Timepoint==0), "Methadone","Naltrexone",subset.ident = "CD14+_Mono", group.by = "Treatment")
Mono_Bup.Nalo_v_Nal_0<-FindMarkers(subset(results, Timepoint==0), "Bup.Nalo","Naltrexone",subset.ident = "CD14+_Mono", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs producedMono_Meth_v_Bup.Nalo_0<-FindMarkers(subset(results, Timepoint==0), "Methadone","Bup.Nalo",subset.ident = "CD14+_Mono", group.by = "Treatment")## Warning in mean.fxn(object[features, cells.2, drop = FALSE]): NaNs producedMono_Meth_v_Nal_3<-FixMotifID(Mono_Meth_v_Nal_3, results)
Mono_Bup.Nalo_v_Nal_3<-FixMotifID(Mono_Bup.Nalo_v_Nal_3, results)
Mono_Meth_v_Bup.Nalo_3<-FixMotifID(Mono_Meth_v_Bup.Nalo_3, results)
Mono_Meth_v_Nal_0<-FixMotifID(Mono_Meth_v_Nal_0, results)
Mono_Bup.Nalo_v_Nal_0<-FixMotifID(Mono_Bup.Nalo_v_Nal_0, results)
Mono_Meth_v_Bup.Nalo_0<-FixMotifID(Mono_Meth_v_Bup.Nalo_0, results)
DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Meth_v_Nal_3)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Bup.Nalo_v_Nal_3)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Meth_v_Bup.Nalo_3)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Meth_v_Nal_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Bup.Nalo_v_Nal_0)DT::datatable(rownames=FALSE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Mono_Meth_v_Bup.Nalo_0)devtools::session_info()## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
## had status 1## - Session info ---------------------------------------------------------------
## setting value
## version R version 4.2.0 (2022-04-22)
## os Red Hat Enterprise Linux 8.8 (Ootpa)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C
## ctype C
## tz Etc/UTC
## date 2023-06-15
## pandoc 3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
##
## - Packages -------------------------------------------------------------------
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [2] CRAN (R 4.2.0)
## BiocGenerics 0.44.0 2022-11-01 [1] Bioconductor
## BiocParallel 1.32.6 2023-03-17 [1] Bioconductor
## Biostrings 2.66.0 2022-11-01 [1] Bioconductor
## bitops 1.0-7 2021-04-24 [2] CRAN (R 4.2.0)
## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.2.0)
## cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.0)
## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0)
## cluster 2.1.4 2022-08-22 [2] CRAN (R 4.2.0)
## codetools 0.2-19 2023-02-01 [2] CRAN (R 4.2.0)
## colorspace 2.1-0 2023-01-23 [2] CRAN (R 4.2.0)
## cowplot 1.1.1 2020-12-30 [2] CRAN (R 4.2.0)
## crayon 1.5.2 2022-09-29 [2] CRAN (R 4.2.0)
## crosstalk 1.2.0 2021-11-04 [2] CRAN (R 4.2.0)
## data.table 1.14.8 2023-02-17 [2] CRAN (R 4.2.0)
## DBI 1.1.3 2022-06-18 [2] CRAN (R 4.2.0)
## deldir 1.0-6 2021-10-23 [2] CRAN (R 4.2.0)
## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.0)
## digest 0.6.31 2022-12-11 [2] CRAN (R 4.2.0)
## dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.2.0)
## DT 0.28 2023-05-18 [1] CRAN (R 4.2.0)
## ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.2.0)
## evaluate 0.20 2023-01-17 [2] CRAN (R 4.2.0)
## fansi 1.0.4 2023-01-22 [2] CRAN (R 4.2.0)
## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0)
## fastmatch 1.1-3 2021-07-23 [2] CRAN (R 4.2.0)
## fitdistrplus 1.1-8 2022-03-10 [2] CRAN (R 4.2.0)
## fs 1.6.1 2023-02-06 [2] CRAN (R 4.2.0)
## future 1.32.0 2023-03-07 [1] CRAN (R 4.2.0)
## future.apply 1.10.0 2022-11-05 [1] CRAN (R 4.2.0)
## generics 0.1.3 2022-07-05 [2] CRAN (R 4.2.0)
## GenomeInfoDb 1.34.9 2023-02-02 [1] Bioconductor
## GenomeInfoDbData 1.2.9 2023-03-17 [1] Bioconductor
## GenomicRanges 1.50.2 2022-12-16 [1] Bioconductor
## ggplot2 * 3.4.2 2023-04-03 [1] CRAN (R 4.2.0)
## ggrepel 0.9.3 2023-02-03 [1] CRAN (R 4.2.0)
## ggridges 0.5.4 2022-09-26 [1] CRAN (R 4.2.0)
## globals 0.16.2 2022-11-21 [1] CRAN (R 4.2.0)
## glue 1.6.2 2022-02-24 [2] CRAN (R 4.2.0)
## goftest 1.2-3 2021-10-07 [2] CRAN (R 4.2.0)
## gridExtra 2.3 2017-09-09 [2] CRAN (R 4.2.0)
## gtable 0.3.3 2023-03-21 [1] CRAN (R 4.2.0)
## htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.2.0)
## htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.2.0)
## httpuv 1.6.9 2023-02-14 [1] CRAN (R 4.2.0)
## httr 1.4.5 2023-02-24 [1] CRAN (R 4.2.0)
## ica 1.0-3 2022-07-08 [2] CRAN (R 4.2.0)
## igraph 1.4.2 2023-04-07 [1] CRAN (R 4.2.0)
## IRanges 2.32.0 2022-11-01 [1] Bioconductor
## irlba 2.3.5.1 2022-10-03 [1] CRAN (R 4.2.0)
## jquerylib 0.1.4 2021-04-26 [2] CRAN (R 4.2.0)
## jsonlite 1.8.4 2022-12-06 [2] CRAN (R 4.2.0)
## KernSmooth 2.23-20 2021-05-03 [2] CRAN (R 4.2.0)
## knitr 1.42 2023-01-25 [1] CRAN (R 4.2.0)
## later 1.3.0 2021-08-18 [2] CRAN (R 4.2.0)
## lattice 0.21-8 2023-04-05 [1] CRAN (R 4.2.0)
## lazyeval 0.2.2 2019-03-15 [2] CRAN (R 4.2.0)
## leiden 0.4.3 2022-09-10 [1] CRAN (R 4.2.0)
## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0)
## limma 3.54.2 2023-02-28 [1] Bioconductor
## listenv 0.9.0 2022-12-16 [2] CRAN (R 4.2.0)
## lmtest 0.9-40 2022-03-21 [2] CRAN (R 4.2.0)
## magrittr 2.0.3 2022-03-30 [2] CRAN (R 4.2.0)
## MASS 7.3-59 2023-04-21 [1] CRAN (R 4.2.0)
## Matrix 1.5-4 2023-04-04 [1] CRAN (R 4.2.0)
## matrixStats 0.63.0 2022-11-18 [2] CRAN (R 4.2.0)
## memoise 2.0.1 2021-11-26 [2] CRAN (R 4.2.0)
## mime 0.12 2021-09-28 [2] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [2] CRAN (R 4.2.0)
## munsell 0.5.0 2018-06-12 [2] CRAN (R 4.2.0)
## nlme 3.1-162 2023-01-31 [1] CRAN (R 4.2.0)
## parallelly 1.35.0 2023-03-23 [1] CRAN (R 4.2.0)
## patchwork * 1.1.2 2022-08-19 [1] CRAN (R 4.2.0)
## pbapply 1.7-0 2023-01-13 [1] CRAN (R 4.2.0)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0)
## pkgbuild 1.4.0 2022-11-27 [1] CRAN (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.2.0)
## pkgload 1.3.2 2022-11-16 [1] CRAN (R 4.2.0)
## plotly 4.10.1 2022-11-07 [1] CRAN (R 4.2.0)
## plyr 1.8.8 2022-11-11 [1] CRAN (R 4.2.0)
## png 0.1-8 2022-11-29 [1] CRAN (R 4.2.0)
## polyclip 1.10-4 2022-10-20 [1] CRAN (R 4.2.0)
## prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.2.0)
## processx 3.8.1 2023-04-18 [1] CRAN (R 4.2.0)
## profvis 0.3.8 2023-05-02 [1] CRAN (R 4.2.0)
## progressr 0.13.0 2023-01-10 [1] CRAN (R 4.2.0)
## promises 1.2.0.1 2021-02-11 [2] CRAN (R 4.2.0)
## ps 1.7.5 2023-04-18 [1] CRAN (R 4.2.0)
## purrr 1.0.1 2023-01-10 [1] CRAN (R 4.2.0)
## R6 2.5.1 2021-08-19 [2] CRAN (R 4.2.0)
## RANN 2.6.1 2019-01-08 [2] CRAN (R 4.2.0)
## RColorBrewer 1.1-3 2022-04-03 [2] CRAN (R 4.2.0)
## Rcpp 1.0.10 2023-01-22 [1] CRAN (R 4.2.0)
## RcppAnnoy 0.0.20 2022-10-27 [1] CRAN (R 4.2.0)
## RcppRoll 0.3.0 2018-06-05 [2] CRAN (R 4.2.0)
## RCurl 1.98-1.12 2023-03-27 [1] CRAN (R 4.2.0)
## remotes 2.4.2 2021-11-30 [2] CRAN (R 4.2.0)
## reshape2 1.4.4 2020-04-09 [2] CRAN (R 4.2.0)
## reticulate 1.28 2023-01-27 [1] CRAN (R 4.2.0)
## rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0)
## rmarkdown 2.22 2023-06-01 [1] CRAN (R 4.2.0)
## ROCR 1.0-11 2020-05-02 [2] CRAN (R 4.2.0)
## Rsamtools 2.14.0 2022-11-01 [1] Bioconductor
## rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.0)
## Rtsne 0.16 2022-04-17 [2] CRAN (R 4.2.0)
## S4Vectors 0.36.2 2023-02-26 [1] Bioconductor
## sass 0.4.5 2023-01-24 [1] CRAN (R 4.2.0)
## scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
## scattermore 0.8 2022-02-14 [1] CRAN (R 4.2.0)
## sctransform 0.3.5 2022-09-21 [1] CRAN (R 4.2.0)
## sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.2.0)
## Seurat * 4.3.0 2022-11-18 [1] CRAN (R 4.2.0)
## SeuratObject * 4.1.3 2022-11-07 [1] CRAN (R 4.2.0)
## shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.0)
## Signac * 1.9.0 2022-12-08 [1] CRAN (R 4.2.0)
## sp 1.6-0 2023-01-19 [1] CRAN (R 4.2.0)
## spatstat.data 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.explore 3.1-0 2023-03-14 [1] CRAN (R 4.2.0)
## spatstat.geom 3.1-0 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.random 3.1-4 2023-03-13 [1] CRAN (R 4.2.0)
## spatstat.sparse 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.utils 3.0-2 2023-03-11 [1] CRAN (R 4.2.0)
## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
## stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
## survival 3.5-5 2023-03-12 [1] CRAN (R 4.2.0)
## tensor 1.5 2012-05-05 [2] CRAN (R 4.2.0)
## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
## uwot 0.1.14 2022-08-22 [1] CRAN (R 4.2.0)
## vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.2.0)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.2.0)
## withr 2.5.0 2022-03-03 [2] CRAN (R 4.2.0)
## xfun 0.39 2023-04-20 [1] CRAN (R 4.2.0)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.2.0)
## XVector 0.38.0 2022-11-01 [1] Bioconductor
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
## zlibbioc 1.44.0 2022-11-01 [1] Bioconductor
## zoo 1.8-12 2023-04-13 [1] CRAN (R 4.2.0)
##
## [1] /gpfs/gibbs/project/ya-chi_ho/jac369/R/4.2
## [2] /vast/palmer/apps/avx2/software/R/4.2.0-foss-2020b/lib64/R/library
##
## ------------------------------------------------------------------------------